import numpy as np 
import pandas as pd 

df = pd.read_csv("C:\\Users\\Hp\\Desktop\\New folder (2)\\click.csv")
df.head()

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Ad Topic Line	City	Gender	Country	Timestamp	Clicked on Ad
0	62.26	32.0	69481.85	172.83	Decentralized real-time circuit	Lisafort	Male	Svalbard & Jan Mayen Islands	2016-06-09 21:43:05	0
1	41.73	31.0	61840.26	207.17	Optional full-range projection	West Angelabury	Male	Singapore	2016-01-16 17:56:05	0
2	44.40	30.0	57877.15	172.83	Total 5thgeneration standardization	Reyesfurt	Female	Guadeloupe	2016-06-29 10:50:45	0
3	59.88	28.0	56180.93	207.17	Balanced empowering success	New Michael	Female	Zambia	2016-06-21 14:32:32	0
4	49.21	30.0	54324.73	201.58	Total 5thgeneration standardization	West Richard	Female	Qatar	2016-07-21 10:54:35	1

import plotly.graph_objects as go
import plotly.express as px 
import plotly.io as pio
pio.templates.default = "plotly_white"

df['Clicked on Ad'] =df['Clicked on Ad'].map({0 :'No',       # Map se replace kiya clicked on ad column data 
                                              1:'Yes'})
df.head()

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Ad Topic Line	City	Gender	Country	Timestamp	Clicked on Ad
0	62.26	32.0	69481.85	172.83	Decentralized real-time circuit	Lisafort	Male	Svalbard & Jan Mayen Islands	2016-06-09 21:43:05	No
1	41.73	31.0	61840.26	207.17	Optional full-range projection	West Angelabury	Male	Singapore	2016-01-16 17:56:05	No
2	44.40	30.0	57877.15	172.83	Total 5thgeneration standardization	Reyesfurt	Female	Guadeloupe	2016-06-29 10:50:45	No
3	59.88	28.0	56180.93	207.17	Balanced empowering success	New Michael	Female	Zambia	2016-06-21 14:32:32	No
4	49.21	30.0	54324.73	201.58	Total 5thgeneration standardization	West Richard	Female	Qatar	2016-07-21 10:54:35	Yes

Now lets analyze the clickt-hrough rate based on the time spent bythe users on the website

fig = px.box(df,
              x ="Daily Time Spent on Site",
              color ="Clicked on Ad",
              title = "Clickt-Through Rate based on The Time Spent on Site ",
               color_discrete_map = {'Yes' :'blue',
                                      'No':'red'})
fig.show()

Now lets analyze the clickt-hrough rate based on the Daily Internet Usage of the users

fig = px.box(df,
              x ="Daily Internet Usage",
              color ="Clicked on Ad",
              title = "Clickt-Through Rate based on The Daily Internet Usage ",
               color_discrete_map = {'Yes' :'blue',
                                      'No':'red'})
fig.show()

Now lets analyze the clickt-hrough rate based on the age of the users

fig = px.box(df,
              x ="Age",
              color ="Clicked on Ad",
              title = "Clickt-Through Rate based on Age ",
               color_discrete_map = {'Yes' :'black',
                                      'No':'red'})
fig.show()

Now lets analyze the clickt-hrough rate based on the Income of the users

fig = px.box(df,
              x ="Area Income",
              color ="Clicked on Ad",
              title = "Clickt-Through Rate based on The Income ",
               color_discrete_map = {'Yes' :'blue',
                                      'No':'red'})
fig.show()

Calculating click Through rate of Ads

df['Clicked on Ad'].value_counts() # valuecount ek column ki jitni bhi subcategory hai unka total finfout karta hai 

No     5083
Yes    4917
Name: Clicked on Ad, dtype: int64

df.shape

(10000, 10)

click_through_rate_no = 5083/10000  #ye humne value nikalni hai 
click_through_rate_no

0.5083

click_through_rate_Yes = 4917/10000
click_through_rate_Yes

0.4917

Click through rate prediction model (Start machine learning )

# yaha per maping karenge convert karenge sare data ko numbrers me
df['Gender'] = df['Gender'].map({'Male':1,
                                  'Female':2,})

x = df.iloc[: ,0:7] #helps us to select a specific row or column from the data set. Using the iloc() function in python
x = x.drop(columns =['Ad Topic Line','City'] , axis = 1) # ye wo column hataye hai jo irrelevant hai jo numbers me nahi (Input data)
y = df.iloc[:,9]   # Target data

from sklearn.model_selection import train_test_split

x_train , x_test , y_train , y_test = train_test_split(x,y,test_size = 0.2 ,random_state =42)#random diya taki figure change na ho 

from sklearn.ensemble import RandomForestClassifier  # Supervise Ml Algoritham 

#rf = RandomForestClassifier() # here

rf = RandomForestClassifier()  # here we will make object of data

rf.fit(x,y)

RandomForestClassifier()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Now lets test the model by making prediction

print("Ads Click Through Rate Prediction ")
a =float(input("Daily Time Spent on Site:"))
b =float(input("Age:"))
c =float(input("Area of Income:"))
d =float(input("Daily Internet Usage:"))  
e =input("Gender (Male = 1, Female = 0):")  


features = np.array([[a,b,c,d,e]])
print("Will the User Click on Ad =" ,rf.predict(features))
               

Ads Click Through Rate Prediction 
Daily Time Spent on Site:74.15
Age:31
Area of Income:59677.64
Daily Internet Usage:207.17
Gender (Male = 1, Female = 0):1
Will the User Click on Ad = ['No']

C:\Users\Hp\anaconda3\Lib\site-packages\sklearn\base.py:464: UserWarning:

X does not have valid feature names, but RandomForestClassifier was fitted with feature names

x_train

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Gender
9254	74.15	31.0	59677.64	207.17	1
1561	72.80	29.0	73910.90	195.69	1
1670	42.04	40.0	39723.97	165.27	2
6087	66.18	49.0	45632.51	124.32	2
6669	44.57	36.0	50628.31	168.92	1
...	...	...	...	...	...
5734	82.07	41.0	57846.68	126.39	2
5191	89.00	36.0	50628.31	126.39	1
5390	78.84	35.0	25603.93	236.87	2
860	59.51	30.0	57877.15	138.71	1
7270	59.05	33.0	52736.33	113.12	1

8000 rows × 5 columns